Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Statistical Opportunities in Neuroimaging

Jian Kang, Thomas Nichols, Lexin Li, Martin A. Lindquist, Hongtu Zhu

TL;DR

It is emphasized that close collaboration among statisticians, neuroscientists, and clinicians is essential for translating neuroimaging advances into improved diagnostics, deeper mechanistic insight, and more personalized treatments.

Abstract

Neuroimaging has profoundly enhanced our understanding of the human brain by characterizing its structure, function, and connectivity through modalities like MRI, fMRI, EEG, and PET. These technologies have enabled major breakthroughs across the lifespan, from early brain development to neurodegenerative and neuropsychiatric disorders. Despite these advances, the brain is a complex, multiscale system, and neuroimaging measurements are correspondingly high-dimensional. This creates major statistical challenges, including measurement noise, motion-related artifacts, substantial inter-subject and site/scanner variability, and the sheer scale of modern studies. This paper explores statistical opportunities and challenges in neuroimaging across four key areas: (i) brain development from birth to age 20, (ii) the adult and aging brain, (iii) neurodegeneration and neuropsychiatric disorders, and (iv) brain encoding and decoding. After a quick tutorial on major imaging technologies, we review cutting-edge studies, underscore data and modeling challenges, and highlight research opportunities for statisticians. We conclude by emphasizing that close collaboration among statisticians, neuroscientists, and clinicians is essential for translating neuroimaging advances into improved diagnostics, deeper mechanistic insight, and more personalized treatments.

Statistical Opportunities in Neuroimaging

TL;DR

It is emphasized that close collaboration among statisticians, neuroscientists, and clinicians is essential for translating neuroimaging advances into improved diagnostics, deeper mechanistic insight, and more personalized treatments.

Abstract

Neuroimaging has profoundly enhanced our understanding of the human brain by characterizing its structure, function, and connectivity through modalities like MRI, fMRI, EEG, and PET. These technologies have enabled major breakthroughs across the lifespan, from early brain development to neurodegenerative and neuropsychiatric disorders. Despite these advances, the brain is a complex, multiscale system, and neuroimaging measurements are correspondingly high-dimensional. This creates major statistical challenges, including measurement noise, motion-related artifacts, substantial inter-subject and site/scanner variability, and the sheer scale of modern studies. This paper explores statistical opportunities and challenges in neuroimaging across four key areas: (i) brain development from birth to age 20, (ii) the adult and aging brain, (iii) neurodegeneration and neuropsychiatric disorders, and (iv) brain encoding and decoding. After a quick tutorial on major imaging technologies, we review cutting-edge studies, underscore data and modeling challenges, and highlight research opportunities for statisticians. We conclude by emphasizing that close collaboration among statisticians, neuroscientists, and clinicians is essential for translating neuroimaging advances into improved diagnostics, deeper mechanistic insight, and more personalized treatments.
Paper Structure (28 sections, 3 figures, 1 table)

This paper contains 28 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Imaging the Human Brain
  3. Brain basics
  4. Whole-brain measurements and models
  5. Electrophysiology Methods
  6. Nuclear Medicine Imaging
  7. Magnetic Resonance Imaging (MRI)
  8. functional Near-Infrared Spectroscopy (fNIRS)
  9. Computed Tomography (CT)
  10. Image Processing Analysis
  11. Case Study 1: Brain Development from Birth to 20 Years
  12. Early Brain Development
  13. Cutting-edge Neuroimaging Studies on Early Brain Development
  14. Data Challenges
  15. Research Opportunities
  16. ...and 13 more sections

Figures (3)

  • Figure 1: The four core MRI analysis components: image reconstruction (forming images from raw data), image enhancement (noise and artifact reduction), image segmentation (delineating structures of interest), and image registration (spatial alignment across subjects, time points, or modalities). These categories represent a conceptual taxonomy rather than an ordered processing pipeline. The bottom row indicates methodological foundations, including statistics, machine learning, optimization, and applied mathematics, that support all four components.
  • Figure 2: Left: Illustration of EEG, fMRI and PET; while both EEG and fMRI capture dynamic brain function, EEG has millisecond resolution while fMRI is 1-2s resolution; PET captures a single time point of brain function but can image a different physiological processes depending on the tracer used. Right: Illustration of spatial and temporal resolutions for different imaging modalities. (EEG traces by Andrii Cherninskyi, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=44035074; resolution figure after Sejnowski2014.)
  • Figure 3: Top: Brain development across lifespan partially extracted from bethlehem2022brain. Bottom: Number of neuroimaging studies sharing neuroimaging data by disease and submission status in the National Institute of Mental Health Data Archive (NDA).